Loading…

Decision-Making Models Based on Incomplete Hesitant Fuzzy Linguistic Preference Relation With Application to Site Selection of Hydropower Stations

This article proposes two decision-making models in an incomplete fuzzy hesitant linguistic environment and applies them to address the site selection problems for hydropower stations. For better depicting a decision maker's judgments under uncertainty, based on the concept of incomplete hesita...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on engineering management 2022-08, Vol.69 (4), p.904-915
Main Authors: Ren, Peijia, Hao, Zhinan, Wang, Xinxin, Zeng, Xiao-Jun, Xu, Zeshui
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article proposes two decision-making models in an incomplete fuzzy hesitant linguistic environment and applies them to address the site selection problems for hydropower stations. For better depicting a decision maker's judgments under uncertainty, based on the concept of incomplete hesitant fuzzy linguistic preference relation, this article first defines its consistency measures from the perspective of additive consistency and multiplicative consistency, respectively. By introducing decision maker's satisfaction degree to measure the differences between the incomplete hesitant fuzzy linguistic preference relation and its corresponding weight vector, two decision-making models, which aim to achieve the maximum satisfaction degree, are established for determining the optimal weight vector. This article further designs experiments to make decision support by evaluating the proposed models from the correlation and time complexity point of view and providing sensitivity analysis. A case study concerning the site selection for a hydropower station at Yalong River is given to illustrate the decision-making process and the effectiveness of the proposed models.
ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2019.2962180